A feasibility study of a computational modeling system for performance evaluation and development of ultrasound strain elastography systems
Bibliographic record
Abstract
Abstract Ultrasound strain elastography (USE) is an imaging technology that enables us to detect changes in tissue stiffness resulting from cancer and other diseases. The objective of this study is to computationally model the application of USE for breast lesion characterization. We develop a well-defined simulation pipeline using open-source software to create in silico USE phantoms with one and two stiff targets. First, we use FreeCAD software for tissue 3D modeling and Gmsh software for finite element (FE) meshes. Second, we place randomly positioned point scatterers within the meshed models to form pre-deformation virtual ultrasound phantoms. Then, a simulated ultrasound transducer is used to compress and deform tissue in FE simulations using FEBio software to create a post-deformation virtual ultrasound phantom. Third, we use the k-Wave acoustics toolbox to generate pre- and post-deformation ultrasound echo signals and B-mode images. Finally, we estimate axial and lateral displacements using a speckle tracking method, and strain elastograms, using a least-squares method. Displacements from the USE simulation pipeline and phantom experiments were compared against true FEBio-simulated displacements for accuracy. We have also quantitatively compared the resultant strain elastograms obtained from FEBio simulations, USE simulation pipeline, and phantom experiments. Finally, model validation is performed by comparing the performance of the USE software platform and physical phantom experiments for a range of compression values (0.5%–5% axial strain). The results confirm the use of the well-validated USE simulation pipeline as a robust non-clinical assessment tool for USE system development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".